Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia.
artificial intelligence
atherosclerosis
coronary computed tomography angiography
coronary ischemia
stress testing
Journal
JACC. Cardiovascular imaging
ISSN: 1876-7591
Titre abrégé: JACC Cardiovasc Imaging
Pays: United States
ID NLM: 101467978
Informations de publication
Date de publication:
29 Feb 2024
29 Feb 2024
Historique:
received:
18
07
2023
revised:
30
11
2023
accepted:
11
01
2024
medline:
14
3
2024
pubmed:
14
3
2024
entrez:
14
3
2024
Statut:
aheadofprint
Résumé
Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCT A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFR In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level as 0.80 (95% CI: 0.75-0.85) for AI-QCT This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.
Sections du résumé
BACKGROUND
BACKGROUND
Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs.
OBJECTIVES
OBJECTIVE
This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCT
METHODS
METHODS
A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFR
RESULTS
RESULTS
In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level as 0.80 (95% CI: 0.75-0.85) for AI-QCT
CONCLUSIONS
CONCLUSIONS
This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.
Identifiants
pubmed: 38483420
pii: S1936-878X(24)00039-1
doi: 10.1016/j.jcmg.2024.01.007
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Investigateurs
Ran Heo
(R)
Hyung-Bok Park
(HB)
Hugo Marques
(H)
Wijnand J Stuijfzand
(WJ)
Jung Hyun Choi
(JH)
Joon-Hyung Doh
(JH)
Ae-Young Her
(AY)
Bon-Kwon Koo
(BK)
Chang-Wook Nam
(CW)
Sang-Hoon Shin
(SH)
Jason Cole
(J)
Alessia Gimelli
(A)
Muhammad Akram Khan
(MA)
Bin Lu
(B)
Yang Gao
(Y)
Faisal Nabi
(F)
Mouaz H Al-Mallah
(MH)
Ryo Nakazato
(R)
U Joseph Schoepf
(UJ)
Randall C Thompson
(RC)
James J Jang
(JJ)
Michael Ridner
(M)
Chris Rowan
(C)
Erick Avelar
(E)
Philippe Généreux
(P)
Guus A de Waard
(GA)
Ralf W Sprengers
(RW)
Pieter G Raijmakers
(PG)
Informations de copyright
Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Funding Support and Author Disclosures This project has been supported by the Foundation “De Drie Lichten” in the Netherlands. Dr Nurmohamed has received grants from the Dutch Heart Foundation (Dekker 03-007-2023-0068) and grants from the European Atherosclerosis Society (2023); and is co-founder of Lipid Tools. Mr Wang, Dr Chan, Ms Crabtree, Ms Aquino, Dr Min, and Dr Earls are employees of Cleerly Inc. Dr Choi has received grant support from GW Heart and Vascular Institute; equity in Cleerly, Inc; and has received consulting fees from Siemens Healthineers. Dr Knaapen has received research grants from HeartFlow, Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.